title: Epilepsy analytic system with cloud computing.
abstract: Biomedical data analytic system has played an important role in doing the clinical diagnosis for several decades. Today, it is an emerging research area of analyzing these big data to make decision support for physicians. This paper presents a parallelized web-based tool with cloud computing service architecture to analyze the epilepsy. There are many modern analytic functions which are wavelet transform, genetic algorithm (GA), and support vector machine (SVM) cascaded in the system. To demonstrate the effectiveness of the system, it has been verified by two kinds of electroencephalography (EEG) data, which are short term EEG and long term EEG. The results reveal that our approach achieves the total classification accuracy higher than 90%. In addition, the entire training time accelerate about 4.66 times and prediction time is also meet requirements in real time.
authors.forename: Chia-Ping
authors.initials: CP
authors.lastname: Shen
authors.forename: Weizhi
authors.initials: W
authors.lastname: Zhou
authors.forename: Feng-Seng
authors.initials: FS
authors.lastname: Lin
authors.forename: Hsiao-Ya
authors.initials: HY
authors.lastname: Sung
authors.forename: Yan-Yu
authors.initials: YY
authors.lastname: Lam
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Jeng-Wei
authors.initials: JW
authors.lastname: Lin
authors.forename: Ming-Kai
authors.initials: MK
authors.lastname: Pan
authors.forename: Ming-Jang
authors.initials: MJ
authors.lastname: Chiu
authors.forename: Feipei
authors.initials: F
authors.lastname: Lai
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D020407:Internet; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D014584:User-Computer Interface; D058067:Wavelet Analysis